Build Your Custom Algorithm, Not Your Data Science Team
A small business should hire an AI consultant for algorithm development when speed to production is critical. It avoids the cost and delay of recruiting, hiring, and onboarding a full-time in-house team.
Syntora assists small businesses with custom algorithm development by providing expertise in full-stack engineering, from data pipelines to scalable deployments. Our approach focuses on building robust production systems with detailed architecture and monitoring, ensuring efficient integration and ongoing performance.
Building a custom algorithm is not just writing a script; it is production engineering. This includes the data pipeline, the API service, deployment infrastructure, and ongoing monitoring. An AI consultant builds this entire system, while an in-house hire often focuses only on the model itself, leaving the difficult integration work undone. The scope of an engagement with Syntora is determined by factors such as the complexity of the desired algorithm, the accessibility and quality of existing data, and the client's internal integration requirements.
What Problem Does This Solve?
The default path is to assign the project to an existing software engineer. While they are proficient in building applications, they often lack experience with the specific failure modes of machine learning systems. They can build a model with scikit-learn but struggle to implement retraining pipelines, monitor for concept drift, or prevent data leakage during training, leading to models that fail silently in production.
A 20-person e-commerce company tasked their backend developer with building a recommendation engine. The developer, an expert in Django and Postgres, spent a month creating a collaborative filtering model. It worked well on a static CSV file but stalled when it came time to productionize. The API response time was over 800ms, too slow for the homepage, and there was no clear path to retrain the model on new user data without hours of downtime. The project was shelved after 3 months.
This happens because building the model is only 10% of the work. The other 90% is the operational infrastructure: data validation, feature engineering pipelines, a low-latency API, automated deployments, and performance monitoring. A small business cannot afford for a generalist developer to spend six months learning this specialized MLOps skillset on a business-critical project.
How Would Syntora Approach This?
Syntora approaches custom algorithm development as a full-stack engineering problem. The first step involves a detailed data audit. We would connect directly to your production data sources, such as a Supabase Postgres database or an analytics event stream. This initial phase defines data requirements, which typically involve historical operational data to train and validate models. We use Polars for high-performance data manipulation and create a data quality report to identify any issues before core development begins. This process requires the client to provide access to relevant data sources and context about data semantics.
The core algorithm logic would be built as a Python service using FastAPI, valued for its speed and asynchronous capabilities. This architectural choice supports efficient handling of concurrent requests and real-time inference needs. Depending on the problem, this could involve developing statistical models, machine learning classifiers like LightGBM, or leveraging large language models such as the Claude API for natural language processing tasks. For example, we've built document processing pipelines using Claude API for financial documents, and the same pattern applies to structuring unstructured data in other domains. The delivered system would expose a clean API endpoint for integration.
The FastAPI application would be containerized with Docker and deployed to AWS Lambda using the Serverless Framework. This provides a scalable, pay-per-use infrastructure, aiming for efficient hosting costs. Automated deployments would be set up from your GitHub repository using GitHub Actions, which runs a full suite of Pytest unit tests to ensure code quality and stability before any new version goes live. Typical build timelines for an algorithm of this complexity, including data pipeline, API, and deployment, range from 6 to 12 weeks, depending on data readiness and algorithm complexity.
Post-deployment, structured logging would be implemented using structlog, sending logs to a dashboard for operational visibility. We configure CloudWatch Alarms to monitor key performance indicators, such as API latency and error rates, with alerts sent to designated personnel via Slack if thresholds are exceeded. For machine learning models, a separate daily job would be configured to recalculate model accuracy against new data and alert if performance degrades, triggering a review or retraining run. Deliverables typically include the deployed production system, source code, and comprehensive documentation.
What Are the Key Benefits?
Live in 4 Weeks, Not 6 Months
We skip the hiring process and internal learning curve. Your custom algorithm is live and integrated into your workflow in a single business cycle.
Fixed Scope, No Hiring Risk
A single project cost avoids the $150k+ annual salary, benefits, and equity of a full-time hire who might not be a good fit.
You Get the Keys and the Blueprint
You receive the full Python source code in your private GitHub repository, along with deployment scripts and a detailed runbook.
Alerts Before Problems Become Outages
We build in monitoring for latency, errors, and model drift. You get a Slack notification if performance degrades, not an angry customer email.
Connects to Your Live Systems
We build REST APIs that integrate with your existing tools. We work with HubSpot webhooks, Shopify APIs, and Supabase Postgres triggers.
What Does the Process Look Like?
Discovery and Data Access (Week 1)
You provide read-only credentials to data sources. We deliver a project plan outlining data quality, success metrics, and a fixed timeline.
Model and API Build (Weeks 2-3)
We build the core algorithm and API. You receive a secure staging URL to test the endpoint with sample data and confirm it meets requirements.
Integration and Deployment (Week 4)
We connect the API to your production systems and deploy it. You receive documentation for the API endpoints and integration logic.
Monitoring and Handoff (Weeks 5-8)
We monitor the live system for 30 days post-launch to ensure stability. You receive the complete runbook and full source code access.
Frequently Asked Questions
- What drives the cost and timeline for a project?
- The main factors are data quality and the number of integration points. A single, clean Postgres database is faster than integrating three third-party APIs with messy schemas. A typical project takes 3-6 weeks from kickoff to production. Book a discovery call at cal.com/syntora/discover for a specific quote based on your requirements.
- What happens if the system breaks after the handoff?
- The runbook we provide covers common failure scenarios and recovery steps any Python developer can follow. For critical systems, we offer an optional monthly support retainer. This includes on-call support with a 4-hour response SLA for production outages, covering bug fixes, dependency updates, and infrastructure issues.
- How is this different from hiring a freelancer on Upwork?
- Most freelancers deliver a model, often in a Jupyter Notebook. Syntora delivers a complete production system. This includes the API, automated deployment, logging, monitoring, and documentation. We build a maintainable business asset, not just experimental code. The person on the discovery call is the engineer who writes every line of code.
- Does our existing engineering team need to be involved?
- Only minimally. They provide data access credentials at the start and can help integrate the final API endpoint at the end. We handle all the MLOps, infrastructure, and deployment, so your team can stay focused on your core product. We aim for less than 5 hours of total involvement from your team.
- What kind of performance improvement can we expect?
- This is defined in the discovery phase. For a lead scoring model, we aim for at least 85% precision on the top 20% of leads. For a route optimization algorithm, we typically reduce drive time by 15-30% compared to manual planning. For demand forecasting, we target a 20-40% reduction in forecast error.
- We don't have a data warehouse. Is that a problem?
- Not at all. For most small businesses, a formal data warehouse is overkill. We work directly with production application databases like Supabase or analytics event streams from tools like Segment. We build lightweight data transformation pipelines as part of the project, so no expensive data infrastructure is required to begin.
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